The following paper introduces a set of novel descriptors of emotional speech, which allows for a significant increase in emotion classification performance. The proposed characteristics - statistical properties of Poincare Maps, derived for voiced-speech segments of utterances - are used in recognition in combinations with a variety of both commonly used and some other, original descriptors of emotional speech. The introduced features proved to provide useful information into a classification process. Emotion recognition is performed using binary decision trees, which perform extraction of different emotions at consecutive decision levels. Classification rates for the considered six-category problem, which involved anger, boredom, joy, fear, neutral and sadness, are at the level up to 79% for both speaker-dependent and speaker-independent cases. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ślot, K., Cichosz, J., & Bronakowski, L. (2008). Emotion recognition with poincare mapping of voiced-speech segments of utterances. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5097 LNAI, pp. 886–895). https://doi.org/10.1007/978-3-540-69731-2_84
Mendeley helps you to discover research relevant for your work.